Intelligent engineering supervision system and method

Abstract

The invention relates to the related technical field of project supervision, and discloses an intelligent project supervision system and method, and the system comprises a distributed data collection module, an edge calculation node, a central processing platform, an intelligent analysis module, a man-machine cooperation terminal, a block chain evidence storage module, and a self-diagnosis maintenance unit. And the distributed data acquisition module. Through multi-source heterogeneous data collaborative acquisition of the visual sensor array and the mechanical sensor group, the purpose of global coverage monitoring of construction quality elements is achieved, and through a star topology connection structure and in combination with a timestamp alignment mechanism of edge computing nodes, data processing delay is reduced to 1 / 5 of that of a traditional architecture, and the construction quality is greatly improved. Moreover, in a formwork support displacement monitoring scene, the early warning response time is reduced, and the matching error of the BIM model and real-time sensing data is controlled through a weight adaptive adjustment algorithm, so that the false alarm rate is greatly reduced in pipe comprehensive collision detection, and the reliability of supervision decision is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field related to engineering supervision, and in particular to an intelligent engineering supervision system and method. Background Art

[0002] Project supervision is an important technical activity in construction projects. Its essence is to provide technical services for professional supervision and management of the entire construction process, and to verify the controllability of project implementation through professional technical means.

[0003] With the expansion of construction project scale and the increase of construction complexity, the traditional engineering supervision model faces severe challenges. The supervision methods currently adopted by the industry mainly have the following technical bottlenecks. Among them, the problem of single data acquisition dimension will lead to a low success rate of the existing system for the simultaneous acquisition of visual data, mechanical parameters, and environmental indicators, resulting in blind spots in quality assessment and being unfavorable for subsequent implementation. In addition, there are certain obstacles to the fusion of multi-source data, which affects the accuracy of three-dimensional scene reconstruction. In order to solve these problems, an intelligent engineering supervision system and method are proposed. Summary of the Invention

[0004] The present invention provides an intelligent engineering supervision system and method, which solve the problems in the above-mentioned background technology.

[0005] The present invention solves the technical problem by adopting the following technical solutions:

[0006] An intelligent engineering supervision system, including a distributed data acquisition module, edge computing nodes, a central processing platform, an intelligent analysis module, a human-machine collaborative terminal, a blockchain evidence storage module, and a self-diagnosis and maintenance unit;

[0007] The distributed data acquisition module includes a visual sensor array, a mechanical sensor group, an environmental monitoring unit, and a construction progress scanner deployed at the construction site;

[0008] The edge computing node establishes a star topology connection with each sensor through an industrial bus and is equipped with a data preprocessing unit and an abnormal data filtering unit;

[0009] The central processing platform is connected to the edge computing nodes through a dual-channel redundant network and includes: a multi-source data fusion engine that uses a time alignment algorithm to process heterogeneous data; a three-dimensional scene reconstruction module that integrates BIM models with real-time sensor data; and a construction specification knowledge graph that stores industry standards and construction process parameters.

[0010] The intelligent analysis module adopts a hybrid architecture neural network model and includes: a quality defect detection unit, a safety risk prediction unit, and a progress deviation analysis unit;

[0011] The human-machine collaborative terminal is equipped with an augmented reality display device and a voice interaction system;

[0012] The blockchain evidence storage module uses a layered encryption structure to store key process data;

[0013] The self-diagnosis and maintenance unit monitors the operating status of system equipment in real time.

[0014] Preferably, the visual sensor array includes a 360-degree panoramic camera, an infrared thermal imager and a structured light three-dimensional scanning device.

[0015] Preferably, the multi-source data fusion engine adopts a sliding window mechanism to achieve spatiotemporal data alignment.

[0016] Preferably, the three-dimensional scene reconstruction module has a dynamic weight adjustment function, which can automatically optimize the matching degree between the BIM model and the measured data.

[0017] Preferably, the human-machine collaborative terminal is provided with an emergency intervention interface, which can be connected to the on-site intelligent construction machinery control system.

[0018] An intelligent engineering supervision method comprises the following steps:

[0019] S1: synchronous acquisition and preprocessing of multimodal data;

[0020] S2: Dynamic reconstruction of 3D scenes and fusion of virtuality and reality;

[0021] S3: Parallel analysis of multi-dimensional engineering indicators;

[0022] S4: Generation and visualization of graded warning information;

[0023] S5: Supervision decision support and process traceability.

[0024] Preferably, a feature weighted fusion algorithm is used in S3 to integrate the quality, safety, and progress assessment results.

[0025] Preferably, the warning level division in S4 is based on a dynamic adjustment threshold of a risk propagation model.

[0026] The advantages and positive effects of the present invention are: through the collaborative collection of multi-source heterogeneous data of the visual sensor array and the mechanical sensor group, the purpose of full-area coverage monitoring of construction quality factors is achieved, and through the star topology connection structure, combined with the timestamp alignment mechanism of the edge computing node, the data processing delay is reduced to 1 / 5 of the traditional architecture, and in the formwork displacement monitoring scenario, the early warning response time is reduced, and through the weight adaptive adjustment algorithm, the matching error between the BIM model and the real-time sensor data is controlled. In the pipe collision detection, the false alarm rate is greatly reduced, which significantly improves the reliability of supervision decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present invention will be further described below with reference to the accompanying drawings and examples.

[0028] Figure 1 It is a schematic diagram of the module structure of the present invention;

[0029] Figure 2 It is a schematic flow chart of the method of the present invention.

[0030] The symbols in the accompanying drawings are described as follows:

[0031] 1. Distributed data acquisition module; 11. Visual sensor array; 111. 360-degree panoramic camera; 112. Infrared thermal imager; 113. Structured light 3D scanning device; 12. Mechanical sensor group; 13. Environmental monitoring unit; 14. Project progress scanner;

[0032] 2. Edge computing node; 21. Data preprocessing unit; 22. Abnormal data filtering unit;

[0033] 3. Dual-channel redundant network;

[0034] 4. Central processing platform; 41. Multi-source data fusion engine; 42. 3D scene reconstruction module; 43. Construction specification knowledge graph;

[0035] 5. Intelligent analysis module; 51. Quality defect detection unit; 52. Safety risk prediction unit; 53. Schedule deviation analysis unit;

[0036] 6. Human-machine collaborative terminal;

[0037] 7. Blockchain evidence storage module;

[0038] 8. Self-diagnosis and maintenance unit. DETAILED DESCRIPTION

[0039] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0040] The embodiments of the present invention are further described below in conjunction with the accompanying drawings:

[0041] Reference Figure 1 and Figure 2As shown in the figure, engineering supervision is an important technical activity in construction projects. Its essence is a technical service for professional supervision and management of the entire process of construction projects, and the controllability verification of engineering implementation is achieved through professional technical means. With the expansion of the scale of construction projects and the increase in construction complexity, the traditional engineering supervision model faces severe challenges. The supervision methods currently used in the industry mainly have the following technical bottlenecks. Among them, the problem of single data acquisition dimension will lead to a low success rate of synchronous acquisition of visual data, mechanical parameters, and environmental indicators by the existing system, resulting in blind spots in quality assessment that are not conducive to subsequent implementation. In addition, there are certain obstacles to the fusion of multi-source data, which affects the accuracy of three-dimensional scene reconstruction. In order to solve such problems, an intelligent engineering supervision system and method are proposed, including a distributed data acquisition module 11, an edge computing node 22, a central processing platform 44, an intelligent analysis module 55, a human-computer collaborative terminal 66, a blockchain evidence storage module 77, and a self-diagnosis and maintenance unit 88;

[0042] The distributed data acquisition module 11 includes a visual sensor array 1111, a mechanical sensor group 1212, an environmental monitoring unit 1313 and a construction progress scanner 1414 deployed at the construction site;

[0043] The edge computing node 22 establishes a star topology connection with each sensor via an industrial bus and is configured with a data preprocessing unit 2121 and an abnormal data filtering unit 2222;

[0044] The central processing platform 44 is connected to the edge computing node 22 via a dual-channel redundant network 33 and includes: a multi-source data fusion engine 4141 that uses a time alignment algorithm to process heterogeneous data; a three-dimensional scene reconstruction module 4242 that integrates BIM models and real-time sensor data; and a construction specification knowledge graph 4343 that stores industry standards and construction process parameters.

[0045] The intelligent analysis module 55 adopts a hybrid architecture neural network model and includes: a quality defect detection unit 5151, a safety risk prediction unit 5252, and a progress deviation analysis unit 5353;

[0046] The human-machine collaborative terminal 66 is equipped with an augmented reality display device and a voice interaction system;

[0047] The blockchain evidence storage module 77 uses a layered encryption structure to store key process data;

[0048] The self-diagnosis and maintenance unit 88 monitors the operating status of system equipment in real time; through the collaborative collection of multi-source heterogeneous data by the visual sensor array 1111 and the mechanical sensor group 1212, the purpose of full-area coverage monitoring of construction quality factors is achieved, and through the star topology connection structure, combined with the timestamp alignment mechanism of the edge computing node 22, the data processing delay is reduced to 1 / 5 of the traditional architecture, and in the formwork displacement monitoring scenario, the early warning response time is reduced, and the weight adaptive adjustment algorithm is used to control the matching error between the BIM model and the real-time sensor data. In the integrated pipe collision detection, the false alarm rate is greatly reduced, which significantly improves the reliability of supervision decisions.

[0049] It should be noted that the visual sensor array 1111 includes a 360-degree panoramic camera 111111, an infrared thermal imager 112112 and a structured light three-dimensional scanning device 113113; the setting of the above-mentioned 360-degree panoramic camera 111111, the infrared thermal imager 112112 and the structured light three-dimensional scanning device 113113 can further improve the perception ability and accuracy of the visual sensor array 1111.

[0050] Furthermore, the multi-source data fusion engine 4141 uses a sliding window mechanism to achieve spatiotemporal data alignment; by achieving spatiotemporal data alignment, a dynamic spatiotemporal compensation mechanism can be achieved: six-degree-of-freedom data calibration is achieved through a sliding window:

[0051] In the time dimension, an adaptive interpolation algorithm is used to compensate for the differences in sampling frequencies of different sensors.

[0052] Secondly, in the spatial dimension: based on the coordinate system transformation matrix, the multi-view observation error is eliminated, so that the three-dimensional point cloud registration accuracy reaches ±1.5mm.

[0053] It should also be noted that the three-dimensional scene reconstruction module 4242 has a dynamic weight adjustment function, which can automatically optimize the matching degree between the BIM model and the measured data; through the weight adaptive adjustment algorithm, the matching error between the BIM model and the real-time sensor data is controlled, and in the comprehensive collision detection of pipes, the false alarm rate is greatly reduced, which significantly improves the reliability of supervision decisions.

[0054] Furthermore, the human-machine collaborative terminal 66 is provided with an emergency intervention interface, which can be connected to the on-site intelligent construction machinery control system; the setting of the emergency intervention interface can facilitate personnel to take over the on-site intelligent construction machinery and avoid unnecessary problems.

[0055] It is worth mentioning that the above-mentioned intelligent engineering supervision methods are as follows:

[0056] S1: synchronous acquisition and preprocessing of multimodal data;

[0057] S2: Dynamic reconstruction of 3D scenes and fusion of virtuality and reality;

[0058] S3: Parallel analysis of multi-dimensional engineering indicators;

[0059] S4: Generation and visualization of graded warning information;

[0060] S5: Supervision decision support and process traceability; The above method can solve the problem of "blind spot monitoring" in traditional supervision, and the overall stability of the supervision system can be guaranteed through the synchronous collection and preprocessing of multimodal data.

[0061] In addition, S3 uses a feature weighted fusion algorithm to integrate quality, safety, and progress evaluation results.

[0062] Additionally, the warning level classification in S4 dynamically adjusts the threshold based on the risk propagation model.

[0063] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention is not limited to the embodiments described in the specific implementation methods. Any other implementation methods derived by those skilled in the art based on the technical solutions of the present invention also fall within the scope of protection of the present invention.

Claims

1. An intelligent engineering supervision system, characterized by: It includes a distributed data acquisition module (1), an edge computing node (2), a central processing platform (4), an intelligent analysis module (5), a human-machine collaborative terminal (6), a blockchain evidence storage module (7), and a self-diagnosis and maintenance unit (8); The distributed data acquisition module (1) comprises a visual sensor array (11), a mechanical sensor group (12), an environmental monitoring unit (13) and a construction progress scanner (14) deployed at the construction site; The edge computing node (2) establishes a star topology connection with each sensor via an industrial bus and is equipped with a data preprocessing unit (21) and an abnormal data filtering unit (22); The central processing platform (4) is connected to the edge computing node (2) via a dual-channel redundant network (3), and includes: a multi-source data fusion engine (41) that uses a time alignment algorithm to process heterogeneous data; a three-dimensional scene reconstruction module (42) that integrates BIM models and real-time sensor data; and a construction specification knowledge graph (43) that stores industry standards and construction process parameters. The intelligent analysis module (5) adopts a hybrid architecture neural network model and includes: a quality defect detection unit (51), a safety risk prediction unit (52), and a progress deviation analysis unit (53); The human-machine collaborative terminal (6) is equipped with an augmented reality display device and a voice interaction system; The blockchain evidence storage module (7) adopts a layered encryption structure to store key process data; The self-diagnosis maintenance unit (8) monitors the operating status of system equipment in real time.

2. The intelligent engineering supervision system according to claim 1, characterized in that: The visual sensor array (11) comprises a 360-degree panoramic camera (111), an infrared thermal imager (112) and a structured light three-dimensional scanning device (113).

3. The intelligent engineering supervision system according to claim 1, characterized in that: The multi-source data fusion engine (41) uses a sliding window mechanism to achieve spatiotemporal data alignment.

4. The intelligent engineering supervision system according to claim 1, characterized in that: The three-dimensional scene reconstruction module (42) has a dynamic weight adjustment function and can automatically optimize the matching degree between the BIM model and the measured data.

5. The intelligent engineering supervision system according to claim 1, characterized in that: The human-machine collaborative terminal (6) is provided with an emergency intervention interface and can be connected to an on-site intelligent construction machinery control system.

6. An intelligent engineering supervision method based on the system according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1: synchronous acquisition and preprocessing of multimodal data; S2: Dynamic reconstruction of 3D scenes and fusion of virtuality and reality; S3: Parallel analysis of multi-dimensional engineering indicators; S4: Generation and visualization of graded warning information; S5: Supervision decision support and process traceability.

7. The intelligent engineering supervision method according to claim 5, characterized in that: S3 uses a feature weighted fusion algorithm to integrate quality, safety, and progress assessment results.

8. The intelligent engineering supervision method according to claim 5, characterized in that: The warning level classification in S4 is based on the dynamic adjustment of thresholds based on the risk propagation model.

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